Abstract
Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.
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